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    "# Understanding Soft Actor-Critic (SAC): A Complete Guide\n",
    "\n",
    "# Table of Contents\n",
    "\n",
    "- [Introduction](#introduction)\n",
    "- [What is SAC?](#what-is-sac)\n",
    "  - [Key Idea: Maximum Entropy RL](#key-idea-maximum-entropy-rl)\n",
    "- [Where and How SAC is Used](#where-and-how-sac-is-used)\n",
    "- [Mathematical Foundation of SAC](#mathematical-foundation-of-sac)\n",
    "  - [The Maximum Entropy Objective](#the-maximum-entropy-objective)\n",
    "  - [Soft State Value Function ($V_{soft}$)](#soft-state-value-function-v_soft)\n",
    "  - [Soft Action Value Function ($Q_{soft}$)](#soft-action-value-function-q_soft)\n",
    "  - [Soft Bellman Backup (Critic Update)](#soft-bellman-backup-critic-update)\n",
    "  - [Policy Improvement (Actor Update)](#policy-improvement-actor-update)\n",
    "  - [Reparameterization Trick](#reparameterization-trick)\n",
    "  - [Entropy Temperature ($\\alpha$) Tuning](#entropy-temperature-\\alpha--tuning)\n",
    "  - [Double Q-Learning and Target Networks](#double-q-learning-and-target-networks)\n",
    "- [Step-by-Step Explanation of SAC](#step-by-step-explanation-of-sac)\n",
    "- [Key Components of SAC](#key-components-of-sac)\n",
    "  - [Stochastic Actor Network (Policy)](#stochastic-actor-network-policy)\n",
    "  - [Critic Networks (Twin Q-Networks)](#critic-networks-twin-q-networks)\n",
    "  - [Target Critic Networks](#target-critic-networks)\n",
    "  - [Entropy Temperature ($\\alpha$)](#entropy-temperature-\\alpha)\n",
    "  - [Replay Buffer](#replay-buffer)\n",
    "  - [Soft Target Updates](#soft-target-updates)\n",
    "  - [Hyperparameters](#hyperparameters)\n",
    "- [Practical Example: Pendulum Environment](#practical-example-pendulum-environment)\n",
    "- [Setting up the Environment](#setting-up-the-environment)\n",
    "- [Creating the Continuous Environment (Gymnasium)](#creating-the-continuous-environment-gymnasium)\n",
    "- [Implementing the SAC Algorithm](#implementing-the-sac-algorithm)\n",
    "  - [Defining the Actor Network (Gaussian Policy)](#defining-the-actor-network-gaussian-policy)\n",
    "  - [Defining the Critic Network (Double Q)](#defining-the-critic-network-double-q)\n",
    "  - [Defining the Replay Memory](#defining-the-replay-memory)\n",
    "  - [Soft Update Function](#soft-update-function)\n",
    "  - [The SAC Update Step](#the-sac-update-step)\n",
    "- [Running the SAC Algorithm](#running-the-sac-algorithm)\n",
    "  - [Hyperparameter Setup](#hyperparameter-setup)\n",
    "  - [Initialization](#initialization)\n",
    "  - [Training Loop](#training-loop)\n",
    "- [Visualizing the Learning Process](#visualizing-the-learning-process)\n",
    "- [Analyzing the Learned Policy (Testing)](#analyzing-the-learned-policy-testing)\n",
    "- [Common Challenges and Solutions in SAC](#common-challenges-and-solutions-in-sac)\n",
    "- [Conclusion](#conclusion)\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Soft Actor-Critic (SAC) is a state-of-the-art **off-policy actor-critic** algorithm designed for **continuous action spaces**, building significantly upon ideas from DDPG, TD3, and incorporating the principle of **maximum entropy reinforcement learning**. Its goal is to learn a policy that maximizes not only the expected cumulative reward but also the policy's entropy. This addition encourages exploration, improves robustness to noise, and often leads to faster and more stable learning compared to previous methods like DDPG and TD3.\n",
    "\n",
    "## What is SAC?\n",
    "\n",
    "SAC learns three main components (often implemented using five networks):\n",
    "\n",
    "1.  **Actor ($\\pi(a|s; \\theta)$):** A *stochastic* policy network that maps states to a probability distribution over actions (typically Gaussian for continuous control). Parameterized by $\\theta$.\n",
    "2.  **Critic (Twin Q-Networks $Q(s, a; \\phi_1), Q(s, a; \\phi_2)$):** Two separate Q-value networks that estimate the *soft* action value (expected return plus entropy). Using two critics helps mitigate Q-value overestimation. Parameterized by $\\phi_1, \\phi_2$.\n",
    "3.  **Entropy Temperature ($\\alpha$):** A positive coefficient that weights the importance of the entropy term in the objective. This can be a fixed hyperparameter or automatically tuned.\n",
    "\n",
    "Like DDPG, it employs:\n",
    "- **Replay Buffer:** For off-policy learning and sample efficiency.\n",
    "- **Target Networks:** Maintains slowly updated target networks for the critic(s) to stabilize Q-learning targets.\n",
    "- **Soft Updates:** Uses slow, smooth updates for the target networks.\n",
    "\n",
    "### Key Idea: Maximum Entropy RL\n",
    "Standard RL aims to maximize the expected sum of discounted rewards: $\\mathbb{E}[\\sum_t \\gamma^t R(s_t, a_t)]$. Maximum Entropy RL modifies this objective to include the policy's entropy at each step:\n",
    "$$ J(\\pi) = \\mathbb{E}_{\\tau \\sim \\pi} \\left[ \\sum_{t=0}^T \\gamma^t \\left( R(s_t, a_t) + \\alpha H(\\pi(\\cdot|s_t)) \\right) \\right] $$\n",
    "where $H(\\pi(\\cdot|s_t))$ is the entropy of the policy distribution at state $s_t$, and $\\alpha$ is the temperature parameter controlling the trade-off between reward maximization and entropy maximization.\n",
    "\n",
    "**Benefits of Maximizing Entropy:**\n",
    "- **Improved Exploration:** The agent is incentivized to explore more diverse actions, potentially discovering better solutions faster and avoiding premature convergence.\n",
    "- **Robustness:** Policies with higher entropy tend to be more robust to perturbations and noise in the environment or execution.\n",
    "- **Compositionality:** Policies learned via max-entropy RL can sometimes be combined more easily for hierarchical tasks.\n",
    "\n",
    "## Where and How SAC is Used\n",
    "\n",
    "SAC is a leading algorithm for continuous control tasks and is widely applied in:\n",
    "\n",
    "1.  **Robotics:** Particularly effective for learning complex locomotion and manipulation skills in simulation (MuJoCo, PyBullet) and increasingly in the real world.\n",
    "2.  **Continuous Control Benchmarks:** Often achieves top performance on standard benchmarks like Pendulum, Hopper, Walker2d, Humanoid, etc.\n",
    "3.  **Autonomous Systems:** Areas requiring smooth, continuous control actions.\n",
    "\n",
    "SAC is suitable when:\n",
    "- The action space is continuous.\n",
    "- High sample efficiency is desired (off-policy).\n",
    "- Robustness and good exploration are important.\n",
    "- Stable learning is preferred over potentially faster but more brittle methods.\n",
    "\n",
    "## Mathematical Foundation of SAC\n",
    "\n",
    "### The Maximum Entropy Objective\n",
    "As stated earlier, the objective is to find a policy $\\pi$ that maximizes:\n",
    "$$ J(\\pi) = \\sum_{t=0}^T \\mathbb{E}_{(s_t, a_t) \\sim \\rho_\\pi} [\\gamma^t (R(s_t, a_t) + \\alpha H(\\pi(\\cdot|s_t)))] $$\n",
    "\n",
    "### Soft State Value Function ($V_{soft}$)\n",
    "In this framework, the value functions are also modified ('softened') to account for the expected future entropy. The soft state value function is defined implicitly via a Bellman equation:\n",
    "$$ V_{soft}^{\\pi}(s) = \\mathbb{E}_{a \\sim \\pi(\\cdot|s)} [ Q_{soft}^{\\pi}(s,a) - \\alpha \\log \\pi(a|s) ] $$\n",
    "It represents the expected return plus expected future entropy starting from state $s$.\n",
    "\n",
    "### Soft Action Value Function ($Q_{soft}$)\n",
    "The soft action value function satisfies the soft Bellman equation:\n",
    "$$ Q_{soft}^{\\pi}(s,a) = R(s,a) + \\gamma \\mathbb{E}_{s' \\sim P} [ V_{soft}^{\\pi}(s') ] $$\n",
    "Substituting the definition of $V_{soft}^{\\pi}(s')$ into the $Q_{soft}^{\\pi}$ equation gives the Bellman backup operator used for training the critic(s):\n",
    "$$ Q_{soft}^{\\pi}(s,a) \\approx R(s,a) + \\gamma \\mathbb{E}_{s' \\sim P, a' \\sim \\pi(\\cdot|s')} [ Q_{soft}^{\\pi}(s', a') - \\alpha \\log \\pi(a'|s') ] $$\n",
    "\n",
    "### Soft Bellman Backup (Critic Update)\n",
    "The critic networks ($Q_{\\phi_1}, Q_{\\phi_2}$) are trained to minimize the soft Bellman residual using samples $(s, a, r, s', d)$ from the replay buffer $\\mathcal{D}$. The target value $y(r, s', d)$ is:\n",
    "$$ y(r, s', d) = r + \\gamma (1-d) \\left( \\min_{i=1,2} Q'_{\\phi'_i}(s', a') - \\alpha \\log \\pi_\theta(a'|s') \\right), \\quad \\text{where } a' \\sim \\pi_\theta(\\cdot|s') $$\n",
    "Note:\n",
    "- $Q'_{\\phi'_i}$ are the *target* critic networks.\n",
    "- $a'$ is sampled from the *current* policy network $\\pi_\theta$ (using the reparameterization trick).\n",
    "- The minimum of the two target Q-values is used (clipped double Q-learning).\n",
    "- $\\alpha$ is the current entropy temperature.\n",
    "\n",
    "The loss for each critic $i=1,2$ is:\n",
    "$$ L(\\phi_i) = \\mathbb{E}_{(s,a,r,s',d) \\sim \\mathcal{D}} [ (Q_{\\phi_i}(s,a) - y(r, s', d))^2 ] $$\n",
    "\n",
    "### Policy Improvement (Actor Update)\n",
    "The actor $\\pi_\theta$ is updated to maximize the expected soft value, which corresponds to minimizing the following loss:\n",
    "$$ L(\theta) = \\mathbb{E}_{s \\sim \\mathcal{D}, a \\sim \\pi_\theta(\\cdot|s)} [ \\alpha \\log \\pi_\theta(a|s) - \\min_{i=1,2} Q_{\\phi_i}(s, a) ] $$\n",
    "This encourages the policy to output actions $a$ that have high soft Q-values and high entropy (low negative log-probability).\n",
    "\n",
    "### Reparameterization Trick\n",
    "To allow gradients to flow from the critic's Q-value estimate back into the actor network parameters $\\theta$ when minimizing $L(\\theta)$, the sampling process $a \\sim \\pi_\theta(\\cdot|s)$ is made differentiable using the reparameterization trick. For a Gaussian policy $\\pi_\theta(\\cdot|s) = \\mathcal{N}(\\mu_\theta(s), \\sigma_\theta(s))$, an action is sampled as:\n",
    "$$ a = \tanh(\\mu_\theta(s) + \\sigma_\theta(s) \\cdot \\xi), \\quad \\xi \\sim \\mathcal{N}(0, I) $$\n",
    "The actor network outputs $\\mu_\theta(s)$ and $\\log \\sigma_\theta(s)$. The $\tanh$ function squashes the output to a bounded range (e.g., [-1, 1]), requiring a correction term when calculating the log-probability $\\log \\pi_\theta(a|s)$ used in the actor and critic target updates.\n",
    "\n",
    "### Entropy Temperature ($\\alpha$) Tuning\n",
    "Instead of fixing $\\alpha$, it can be learned automatically by defining a target entropy $\\bar{H}$ (often set heuristically, e.g., $-\\text{dim}(\\mathcal{A})$) and optimizing $\\alpha$ to match the policy's average entropy to this target. The loss for $\\alpha$ (optimized via its logarithm $\\log \\alpha$) is:\n",
    "$$ L(\\log \\alpha) = \\mathbb{E}_{a_t \\sim \\pi_\theta} [ -\\log \\alpha ( \\log \\pi_\theta(a_t|s_t) + \\bar{H} )^{\\text{detached}} ] $$\n",
    "Minimizing this loss via gradient descent adjusts $\\alpha$: if entropy is too low, $\\alpha$ increases; if too high, $\\alpha$ decreases.\n",
    "\n",
    "### Double Q-Learning and Target Networks\n",
    "- **Double Q-Learning:** Using two critic networks ($Q_1, Q_2$) and taking the minimum of their target values helps reduce the overestimation bias common in Q-learning.\n",
    "- **Target Networks:** Separate target networks ($Q'_1, Q'_2$) are used for calculating the Bellman target $y$. They are updated slowly using soft updates ($\tau$) towards the main critic networks.\n",
    "\n",
    "## Step-by-Step Explanation of SAC\n",
    "\n",
    "1.  **Initialize**: Actor network $\\pi_\theta$, two Critic networks $Q_{\\phi_1}, Q_{\\phi_2}$.\n",
    "2.  **Initialize**: Two Target Critic networks $Q'_{\\phi'_1}, Q'_{\\phi'_2}$ with $\\phi'_1 \\leftarrow \\phi_1$, $\\phi'_2 \\leftarrow \\phi_2$.\n",
    "3.  **Initialize**: Replay buffer $\\mathcal{D}$.\n",
    "4.  **Initialize**: Entropy temperature $\\alpha$ (either fixed or learnable via $\\log \\alpha$). If learnable, initialize target entropy $\\bar{H}$ and $\\log \\alpha$ optimizer.\n",
    "5.  **Initialize**: Actor and Critic optimizers.\n",
    "6.  **For each episode**:\n",
    "    a.  Reset environment, get initial state $s_0$.\n",
    "    b.  **For each step $t$**:\n",
    "        i.   Sample action $a_t \\sim \\pi_\theta(\\cdot|s_t)$ (using reparameterization trick, add noise only if needed - stochastic policy explores).\n",
    "        ii.  Execute $a_t$, observe reward $r_t$, next state $s_{t+1}$, done flag $d_t$.\n",
    "        iii. Store transition $(s_t, a_t, r_t, s_{t+1}, d_t)$ in $\\mathcal{D}$.\n",
    "        iv.  **Update Step (after collecting enough data, e.g., every step or few steps)**:\n",
    "            1.  Sample mini-batch of $N$ transitions from $\\mathcal{D}$.\n",
    "            2.  **Update Critic(s)**:\n",
    "                - Compute target values $y_j$ using target critics, current actor, and $\\alpha$.\n",
    "                - Minimize MSE loss $L(\\phi_1)$ and $L(\\phi_2)$ via gradient descent.\n",
    "            3.  **Update Actor**: \n",
    "                - Compute actor loss $L(\\theta)$ using current critics and current actor.\n",
    "                - Minimize $L(\\theta)$ via gradient descent.\n",
    "            4.  **Update Alpha (if tuning)**:\n",
    "                - Compute alpha loss $L(\\log \\alpha)$.\n",
    "                - Minimize $L(\\log \\alpha)$ via gradient descent.\n",
    "            5.  **Update Target Critics**: Perform soft updates:\n",
    "                 $\\phi'_i \\leftarrow \\tau \\phi_i + (1 - \\tau) \\phi'_i$ for $i=1,2$.\n",
    "        v.   $s_t \\leftarrow s_{t+1}$.\n",
    "        vi.  If $d_t$, break episode.\n",
    "7.  **Repeat**: Until convergence or max episodes/steps.\n",
    "\n",
    "## Key Components of SAC\n",
    "\n",
    "### Stochastic Actor Network (Policy)\n",
    "- Maps state $s$ to parameters (e.g., mean, log std dev) of a distribution (e.g., Gaussian) over actions $a$.\n",
    "- Trained to maximize soft Q-values and entropy.\n",
    "- Uses reparameterization trick for differentiability.\n",
    "\n",
    "### Critic Networks (Twin Q-Networks)\n",
    "- Two networks ($Q_1, Q_2$) estimating the soft action value $Q_{soft}(s,a)$.\n",
    "- Trained using soft Bellman targets.\n",
    "\n",
    "### Target Critic Networks\n",
    "- Slowly updated copies ($Q'_1, Q'_2$) used to compute stable Bellman targets.\n",
    "\n",
    "### Entropy Temperature ($\\alpha$)\n",
    "- Balances reward vs. entropy maximization. Can be fixed or automatically tuned.\n",
    "\n",
    "### Replay Buffer\n",
    "- Standard off-policy buffer storing transitions.\n",
    "\n",
    "### Soft Target Updates\n",
    "- Slow blending of main critic parameters into target critic parameters ($\tau$).\n",
    "\n",
    "### Hyperparameters\n",
    "- Buffer size, batch size, learning rates (actor, critic, alpha).\n",
    "- Target update rate ($\tau$), discount ($\\gamma$).\n",
    "- Initial $\\alpha$ and target entropy $\\bar{H}$ (if tuning $\\alpha$).\n",
    "- Network architectures.\n",
    "\n",
    "## Practical Example: Pendulum Environment\n",
    "\n",
    "We use `Pendulum-v1` from Gymnasium to demonstrate SAC in a continuous action space. **Requires `gymnasium`.**"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practical Example: Pendulum Environment\n",
    "\n",
    "![Pendulum]()\n",
    "\n",
    "### Why Pendulum? (Continuous Actions and Exploration)\n",
    "\n",
    "SAC is well-suited for environments with continuous action spaces like `Pendulum-v1`. While Grid World offers discrete actions (Up, Down, Left, Right), SAC excels when the agent needs to learn fine-grained control.  The `Pendulum-v1` environment is a standard benchmark, advantageous for demonstrating SAC's capabilities because:\n",
    "\n",
    "- **Continuous State:** `[cos(theta), sin(theta), theta_dot]` represents the pendulum's state (angle and angular velocity).\n",
    "\n",
    "- **Continuous Action:** Torque applied to the pendulum's joint.  This is a single continuous value, typically within the range of [-2.0, 2.0].\n",
    "\n",
    "- **Emphasis on Exploration:** SAC inherently encourages exploration by maximizing entropy alongside reward.  This is particularly useful in the `Pendulum-v1` environment, where finding the optimal control strategy often requires trying different levels of torque and observing the resulting dynamics.  The continuous action space provides the agent with a vast range of possible behaviors to explore.\n",
    "\n",
    "Using this environment allows us to correctly demonstrate SAC's handling of continuous actions and its exploration strategy.  **This requires the `gymnasium` library, deviating slightly from the 'basic libraries only' constraint of the reference DQN notebook, as SAC is fundamentally designed for continuous action spaces where exploration is key.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up the Environment\n",
    "\n",
    "Import libraries, including `gymnasium`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cpu\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\faree\\Desktop\\all-rl-algorithms\\.venv-all-rl-algos\\lib\\site-packages\\torch\\__init__.py:1236: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\torch\\csrc\\tensor\\python_tensor.cpp:436.)\n",
      "  _C._set_default_tensor_type(t)\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import math\n",
    "from collections import namedtuple, deque\n",
    "from itertools import count\n",
    "from typing import List, Tuple, Dict, Optional, Callable, Any\n",
    "import copy\n",
    "\n",
    "# Import PyTorch\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.distributions import Normal # Use Normal distribution for continuous actions\n",
    "\n",
    "torch.set_default_tensor_type(torch.FloatTensor)  # Set default to float32\n",
    "\n",
    "# Import Gymnasium\n",
    "try:\n",
    "    import gymnasium as gym\n",
    "except ImportError:\n",
    "    print(\"Gymnasium not found. Please install using 'pip install gymnasium' or 'pip install gym[classic_control]'\")\n",
    "    gym = None\n",
    "\n",
    "# Set up device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# Set random seeds\n",
    "seed = 42\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the Continuous Environment (Gymnasium)\n",
    "\n",
    "Instantiate Pendulum-v1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pendulum Environment:\n",
      "State Dim: 3\n",
      "Action Dim: 1\n",
      "Action Low: -2.0\n",
      "Action High: 2.0\n"
     ]
    }
   ],
   "source": [
    "# Instantiate the Pendulum environment\n",
    "if gym is not None:\n",
    "    try:\n",
    "        env = gym.make('Pendulum-v1')\n",
    "        env.reset(seed=seed)\n",
    "        env.action_space.seed(seed)\n",
    "\n",
    "        n_observations_sac = env.observation_space.shape[0]\n",
    "        n_actions_sac = env.action_space.shape[0]\n",
    "        action_low_sac = env.action_space.low[0]\n",
    "        action_high_sac = env.action_space.high[0]\n",
    "\n",
    "        print(f\"Pendulum Environment:\")\n",
    "        print(f\"State Dim: {n_observations_sac}\")\n",
    "        print(f\"Action Dim: {n_actions_sac}\")\n",
    "        print(f\"Action Low: {action_low_sac}\")\n",
    "        print(f\"Action High: {action_high_sac}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error creating Gymnasium environment: {e}\")\n",
    "        n_observations_sac = 3\n",
    "        n_actions_sac = 1\n",
    "        action_low_sac = -2.0\n",
    "        action_high_sac = 2.0\n",
    "        env = None\n",
    "else:\n",
    "    print(\"Gymnasium not available. Cannot create Pendulum environment.\")\n",
    "    n_observations_sac = 3\n",
    "    n_actions_sac = 1\n",
    "    action_low_sac = -2.0\n",
    "    action_high_sac = 2.0\n",
    "    env = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing the SAC Algorithm\n",
    "\n",
    "Define the SAC components: Actor, Critic, Replay Buffer, Update logic.\n",
    "\n",
    "### Defining the Actor Network (Gaussian Policy)\n",
    "\n",
    "Outputs mean and log standard deviation for a Gaussian distribution. Actions are sampled, squashed with `tanh`, and log probabilities are corrected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOG_STD_MAX = 2\n",
    "LOG_STD_MIN = -20\n",
    "EPSILON = 1e-6 # Small number for numerical stability\n",
    "\n",
    "class ActorNetworkSAC(nn.Module):\n",
    "    \"\"\" Stochastic Gaussian Actor Network for SAC \"\"\"\n",
    "    def __init__(self, n_observations: int, n_actions: int, action_high_bound: float):\n",
    "        super(ActorNetworkSAC, self).__init__()\n",
    "        self.action_high_bound = action_high_bound\n",
    "        # Architecture (adjust complexity as needed)\n",
    "        self.layer1 = nn.Linear(n_observations, 256)\n",
    "        self.layer2 = nn.Linear(256, 256)\n",
    "        self.mean_layer = nn.Linear(256, n_actions) # Outputs mean\n",
    "        self.log_std_layer = nn.Linear(256, n_actions) # Outputs log standard deviation\n",
    "\n",
    "    def forward(self, state: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n",
    "        \"\"\"\n",
    "        Outputs action and its log probability, using reparameterization and tanh squashing.\n",
    "        Parameters:\n",
    "        - state (torch.Tensor): Input state.\n",
    "        Returns:\n",
    "        - Tuple[torch.Tensor, torch.Tensor]:\n",
    "            - action: Squashed action sampled from the policy.\n",
    "            - log_prob: Log probability of the squashed action.\n",
    "        \"\"\"\n",
    "        # Check if state is a single sample and add batch dimension if needed\n",
    "        add_batch_dim = False\n",
    "        if state.dim() == 1:\n",
    "            state = state.unsqueeze(0)  # Add batch dimension\n",
    "            add_batch_dim = True\n",
    "            \n",
    "        x = F.relu(self.layer1(state))\n",
    "        x = F.relu(self.layer2(x))\n",
    "        \n",
    "        mean = self.mean_layer(x)\n",
    "        log_std = self.log_std_layer(x)\n",
    "        # Clamp log_std for stability\n",
    "        log_std = torch.clamp(log_std, LOG_STD_MIN, LOG_STD_MAX)\n",
    "        std = torch.exp(log_std)\n",
    "\n",
    "        # Create Gaussian distribution\n",
    "        normal_dist = Normal(mean, std)\n",
    "\n",
    "        # Reparameterization trick: sample pre-squashed action\n",
    "        # Use rsample() for differentiable sampling\n",
    "        z = normal_dist.rsample()\n",
    "        \n",
    "        # Apply tanh squashing to get bounded action\n",
    "        action = torch.tanh(z)\n",
    "        \n",
    "        # Calculate log-probability with correction for tanh squashing\n",
    "        # log_prob = log_normal(z) - log(1 - tanh(z)^2)\n",
    "        log_prob = normal_dist.log_prob(z) - torch.log(1 - action.pow(2) + EPSILON)\n",
    "        \n",
    "        # Sum across action dimensions (proper handling of dimensions)\n",
    "        if log_prob.dim() > 1:\n",
    "            log_prob = log_prob.sum(dim=1, keepdim=True)\n",
    "        else:\n",
    "            log_prob = log_prob.sum(keepdim=True)\n",
    "        \n",
    "        # Scale action to environment bounds\n",
    "        action = action * self.action_high_bound\n",
    "        \n",
    "        # Remove batch dimension if it was added\n",
    "        if add_batch_dim:\n",
    "            action = action.squeeze(0)\n",
    "            log_prob = log_prob.squeeze(0)\n",
    "            \n",
    "        return action, log_prob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Critic Network (Double Q)\n",
    "\n",
    "Contains two separate Q-networks internally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CriticNetworkSAC(nn.Module):\n",
    "    \"\"\" Twin Q-Value Critic Network for SAC \"\"\"\n",
    "    def __init__(self, n_observations: int, n_actions: int):\n",
    "        super(CriticNetworkSAC, self).__init__()\n",
    "\n",
    "        # Q1 Architecture\n",
    "        self.q1_layer1 = nn.Linear(n_observations + n_actions, 256)\n",
    "        self.q1_layer2 = nn.Linear(256, 256)\n",
    "        self.q1_output = nn.Linear(256, 1)\n",
    "\n",
    "        # Q2 Architecture\n",
    "        self.q2_layer1 = nn.Linear(n_observations + n_actions, 256)\n",
    "        self.q2_layer2 = nn.Linear(256, 256)\n",
    "        self.q2_output = nn.Linear(256, 1)\n",
    "\n",
    "    def forward(self, state: torch.Tensor, action: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n",
    "        \"\"\"\n",
    "        Outputs the Q-values from both internal critics.\n",
    "        Parameters:\n",
    "        - state (torch.Tensor): Input state tensor.\n",
    "        - action (torch.Tensor): Input action tensor.\n",
    "        Returns:\n",
    "        - Tuple[torch.Tensor, torch.Tensor]: Q1(s, a) and Q2(s, a).\n",
    "        \"\"\"\n",
    "        sa = torch.cat([state, action], dim=1) # Concatenate state and action\n",
    "\n",
    "        # Q1 forward pass\n",
    "        q1 = F.relu(self.q1_layer1(sa))\n",
    "        q1 = F.relu(self.q1_layer2(q1))\n",
    "        q1 = self.q1_output(q1)\n",
    "\n",
    "        # Q2 forward pass\n",
    "        q2 = F.relu(self.q2_layer1(sa))\n",
    "        q2 = F.relu(self.q2_layer2(q2))\n",
    "        q2 = self.q2_output(q2)\n",
    "\n",
    "        return q1, q2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Replay Memory\n",
    "\n",
    "Standard buffer, identical to DDPG/DQN version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the structure for storing transitions\n",
    "# Using the same Transition namedtuple as DDPG/DQN\n",
    "Transition = namedtuple('Transition',\n",
    "                        ('state', 'action', 'reward', 'next_state', 'done'))\n",
    "\n",
    "# Define the Replay Memory class (Identical to DDPG/DQN version)\n",
    "class ReplayMemory(object):\n",
    "    def __init__(self, capacity: int):\n",
    "        self.memory = deque([], maxlen=capacity)\n",
    "\n",
    "    def push(self, *args: Any) -> None:\n",
    "        processed_args = []\n",
    "        for arg in args:\n",
    "            if isinstance(arg, torch.Tensor):\n",
    "                # Ensure tensors are float32 and on CPU\n",
    "                processed_args.append(arg.to(torch.float32).cpu())\n",
    "            elif isinstance(arg, np.ndarray):\n",
    "                # Convert numpy array to float32 tensor\n",
    "                processed_args.append(torch.from_numpy(arg).to(torch.float32).cpu())\n",
    "            elif isinstance(arg, (bool, float, int)):\n",
    "                # Store scalar values as float32 tensors\n",
    "                processed_args.append(torch.tensor([arg], dtype=torch.float32))\n",
    "            else:\n",
    "                processed_args.append(arg)\n",
    "        self.memory.append(Transition(*processed_args))\n",
    "\n",
    "    def sample(self, batch_size: int) -> List[Transition]:\n",
    "        return random.sample(self.memory, batch_size)\n",
    "\n",
    "    def __len__(self) -> int:\n",
    "        return len(self.memory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soft Update Function\n",
    "\n",
    "Reusing the soft update function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def soft_update(target_net: nn.Module, main_net: nn.Module, tau: float) -> None:\n",
    "    \"\"\" Performs a soft update of the target network parameters. (Identical) \"\"\"\n",
    "    for target_param, main_param in zip(target_net.parameters(), main_net.parameters()):\n",
    "        target_param.data.copy_(tau * main_param.data + (1.0 - tau) * target_param.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The SAC Update Step\n",
    "\n",
    "Performs the critic, actor, and (optionally) alpha updates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_sac(memory: ReplayMemory,\n",
    "               batch_size: int,\n",
    "               actor: ActorNetworkSAC,\n",
    "               critic: CriticNetworkSAC,\n",
    "               target_critic: CriticNetworkSAC,\n",
    "               actor_optimizer: optim.Optimizer,\n",
    "               critic_optimizer: optim.Optimizer,\n",
    "               log_alpha: torch.Tensor,\n",
    "               alpha_optimizer: optim.Optimizer,\n",
    "               target_entropy: float,\n",
    "               gamma: float,\n",
    "               tau: float) -> Tuple[float, float, float, float]:\n",
    "    \"\"\"\n",
    "    Performs one SAC update step (critic, actor, alpha).\n",
    "    \"\"\"\n",
    "    # Ensure enough samples are available in memory\n",
    "    if len(memory) < batch_size:\n",
    "        return 0.0, 0.0, 0.0, torch.exp(log_alpha.detach()).item()\n",
    "\n",
    "    # Sample a batch of transitions from memory\n",
    "    transitions = memory.sample(batch_size)\n",
    "    batch = Transition(*zip(*transitions))\n",
    "\n",
    "    # Unpack and move data to the appropriate device with explicit dtype=float32\n",
    "    state_batch = torch.cat([s.view(1, -1).float() for s in batch.state]).to(device)\n",
    "    action_batch = torch.cat([a.view(1, -1).float() for a in batch.action]).to(device)\n",
    "    reward_batch = torch.cat([r.view(1, -1).float() for r in batch.reward]).to(device)\n",
    "    next_state_batch = torch.cat([s.view(1, -1).float() for s in batch.next_state]).to(device)\n",
    "    done_batch = torch.cat([d.view(1, -1).float() for d in batch.done]).to(device)\n",
    "\n",
    "    # --- Critic Update ---\n",
    "    with torch.no_grad():\n",
    "        # Get next action and log probability from the current policy\n",
    "        next_action, next_log_prob = actor(next_state_batch)\n",
    "        \n",
    "        # Get target Q values from the target critics\n",
    "        q1_target_next, q2_target_next = target_critic(next_state_batch, next_action)\n",
    "        q_target_next = torch.min(q1_target_next, q2_target_next)  # Min of two Q-values\n",
    "        \n",
    "        # Calculate the soft target:\n",
    "        # soft_target = Q_target_next - α * log_prob\n",
    "        alpha = torch.exp(log_alpha.detach()).float()\n",
    "        soft_target = q_target_next - alpha * next_log_prob\n",
    "        \n",
    "        # Compute the target value for the Bellman equation:\n",
    "        # y = reward + γ * (1 - done) * soft_target\n",
    "        y = reward_batch + gamma * (1.0 - done_batch) * soft_target\n",
    "\n",
    "    # Get current Q estimates from the critic\n",
    "    q1_current, q2_current = critic(state_batch, action_batch)\n",
    "\n",
    "    # Calculate critic losses (Mean Squared Error):\n",
    "    # critic_loss = MSE(Q1_current, y) + MSE(Q2_current, y)\n",
    "    critic1_loss = F.mse_loss(q1_current, y)\n",
    "    critic2_loss = F.mse_loss(q2_current, y)\n",
    "    critic_loss = critic1_loss + critic2_loss\n",
    "\n",
    "    # Optimize the critic networks\n",
    "    critic_optimizer.zero_grad()\n",
    "    critic_loss.backward()\n",
    "    critic_optimizer.step()\n",
    "\n",
    "    # --- Actor Update ---\n",
    "    # Freeze critic gradients to avoid updating them during actor optimization\n",
    "    for p in critic.parameters():\n",
    "        p.requires_grad = False\n",
    "\n",
    "    # Get actions and log probabilities for the current states from the actor\n",
    "    pi_action, pi_log_prob = actor(state_batch)\n",
    "    \n",
    "    # Get Q values for these actions from the critic\n",
    "    q1_pi, q2_pi = critic(state_batch, pi_action)\n",
    "    min_q_pi = torch.min(q1_pi, q2_pi)  # Min of two Q-values\n",
    "\n",
    "    # Calculate actor loss:\n",
    "    # actor_loss = E[α * log_prob - Q_min]\n",
    "    actor_loss = (alpha * pi_log_prob - min_q_pi).mean()\n",
    "\n",
    "    # Optimize the actor network\n",
    "    actor_optimizer.zero_grad()\n",
    "    actor_loss.backward()\n",
    "    actor_optimizer.step()\n",
    "\n",
    "    # Unfreeze critic gradients\n",
    "    for p in critic.parameters():\n",
    "        p.requires_grad = True\n",
    "        \n",
    "    # --- Alpha (Entropy Temperature) Update ---\n",
    "    # Calculate alpha loss:\n",
    "    # alpha_loss = -E[log_alpha * (log_prob + target_entropy)]\n",
    "    target_entropy_tensor = torch.tensor(target_entropy, dtype=torch.float32, device=device)\n",
    "    alpha_loss = -(log_alpha * (pi_log_prob.detach().float() + target_entropy_tensor)).mean()\n",
    "\n",
    "    # Optimize alpha (if auto-tuning is enabled)\n",
    "    if alpha_optimizer is not None:\n",
    "        alpha_optimizer.zero_grad()\n",
    "        alpha_loss.backward()\n",
    "        alpha_optimizer.step()\n",
    "    \n",
    "    # Get the current value of alpha\n",
    "    current_alpha = torch.exp(log_alpha.detach()).item()\n",
    "\n",
    "    # --- Update Target Networks ---\n",
    "    # Perform a soft update of the target critic networks:\n",
    "    # θ_target = τ * θ_main + (1 - τ) * θ_target\n",
    "    soft_update(target_critic, critic, tau)\n",
    "\n",
    "    # Return the losses and the current alpha value\n",
    "    return critic_loss.item(), actor_loss.item(), alpha_loss.item(), current_alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the SAC Algorithm\n",
    "\n",
    "Set up hyperparameters, initialize everything, and run the SAC training loop.\n",
    "\n",
    "### Hyperparameter Setup\n",
    "\n",
    "Define SAC hyperparameters for Pendulum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters for SAC on Pendulum-v1\n",
    "BUFFER_SIZE_SAC = int(1e6)     # Replay buffer capacity\n",
    "BATCH_SIZE_SAC = 256           # Mini-batch size\n",
    "GAMMA_SAC = 0.99               # Discount factor\n",
    "TAU_SAC = 5e-3                 # Soft update factor\n",
    "LR_SAC = 3e-4                  # Learning rate for actor, critic, and alpha\n",
    "INITIAL_ALPHA = 0.2            # Initial entropy temperature (or fixed value if not tuning)\n",
    "AUTO_TUNE_ALPHA = True         # Whether to automatically tune alpha\n",
    "TARGET_ENTROPY = -float(n_actions_sac) # Heuristic target entropy: -|Action Space Dim|\n",
    "\n",
    "NUM_EPISODES_SAC = 100         # Number of training episodes\n",
    "MAX_STEPS_PER_EPISODE_SAC = 200 # Pendulum typically uses 200 steps\n",
    "START_STEPS = 1000             # Number of initial random steps before training starts\n",
    "UPDATE_EVERY_SAC = 1           # Perform update after every environment step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialization\n",
    "\n",
    "Initialize all networks, target networks, optimizers, alpha, and buffer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "if env is None:\n",
    "    raise RuntimeError(\"Gymnasium environment 'Pendulum-v1' could not be created.\")\n",
    "\n",
    "# Initialize Networks\n",
    "actor_sac = ActorNetworkSAC(n_observations_sac, n_actions_sac, action_high_sac).to(device)\n",
    "critic_sac = CriticNetworkSAC(n_observations_sac, n_actions_sac).to(device)\n",
    "target_critic_sac = CriticNetworkSAC(n_observations_sac, n_actions_sac).to(device)\n",
    "target_critic_sac.load_state_dict(critic_sac.state_dict())\n",
    "# Freeze target critic parameters\n",
    "for p in target_critic_sac.parameters():\n",
    "    p.requires_grad = False\n",
    "\n",
    "# Initialize Optimizers\n",
    "actor_optimizer_sac = optim.Adam(actor_sac.parameters(), lr=LR_SAC)\n",
    "critic_optimizer_sac = optim.Adam(critic_sac.parameters(), lr=LR_SAC)\n",
    "\n",
    "# Initialize Alpha (Entropy Temperature)\n",
    "# Initialize Alpha (Entropy Temperature) with explicit dtype\n",
    "if AUTO_TUNE_ALPHA:\n",
    "    # Learn log_alpha for stability with explicit float32\n",
    "    log_alpha_sac = torch.tensor(np.log(INITIAL_ALPHA), dtype=torch.float32, requires_grad=True, device=device)\n",
    "    alpha_optimizer_sac = optim.Adam([log_alpha_sac], lr=LR_SAC)\n",
    "else:\n",
    "    log_alpha_sac = torch.tensor(np.log(INITIAL_ALPHA), dtype=torch.float32, requires_grad=False, device=device)\n",
    "    alpha_optimizer_sac = None # No optimizer needed if alpha is fixed\n",
    "\n",
    "# Make sure TARGET_ENTROPY is also float32\n",
    "TARGET_ENTROPY_TENSOR = torch.tensor(-float(n_actions_sac), dtype=torch.float32, device=device)\n",
    "\n",
    "# Initialize Replay Memory\n",
    "memory_sac = ReplayMemory(BUFFER_SIZE_SAC)\n",
    "\n",
    "# Lists for plotting\n",
    "sac_episode_rewards = []\n",
    "sac_episode_critic_losses = []\n",
    "sac_episode_actor_losses = []\n",
    "sac_episode_alpha_losses = []\n",
    "sac_episode_alphas = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loop\n",
    "\n",
    "The SAC training loop, including initial random exploration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting SAC Training on Pendulum-v1...\n",
      "Ep 10/100 | Steps: 2000 | Avg Reward: -1413.71 | C_Loss: 13.7941 | A_Loss: 9.2109 | Alpha: 0.1522\n",
      "Ep 20/100 | Steps: 4000 | Avg Reward: -1133.37 | C_Loss: 34.4909 | A_Loss: 61.2778 | Alpha: 0.1203\n",
      "Ep 30/100 | Steps: 6000 | Avg Reward: -783.47 | C_Loss: 89.4786 | A_Loss: 95.3463 | Alpha: 0.1385\n",
      "Ep 40/100 | Steps: 8000 | Avg Reward: -201.32 | C_Loss: 133.6666 | A_Loss: 109.4018 | Alpha: 0.2055\n",
      "Ep 50/100 | Steps: 10000 | Avg Reward: -208.57 | C_Loss: 147.7181 | A_Loss: 109.8792 | Alpha: 0.2450\n",
      "Ep 60/100 | Steps: 12000 | Avg Reward: -137.20 | C_Loss: 162.6894 | A_Loss: 102.9486 | Alpha: 0.2321\n",
      "Ep 70/100 | Steps: 14000 | Avg Reward: -183.97 | C_Loss: 150.2266 | A_Loss: 93.0447 | Alpha: 0.2226\n",
      "Ep 80/100 | Steps: 16000 | Avg Reward: -168.13 | C_Loss: 132.5991 | A_Loss: 82.4845 | Alpha: 0.1889\n",
      "Ep 90/100 | Steps: 18000 | Avg Reward: -127.96 | C_Loss: 120.6159 | A_Loss: 71.2044 | Alpha: 0.1632\n",
      "Ep 100/100 | Steps: 20000 | Avg Reward: -155.37 | C_Loss: 113.2320 | A_Loss: 61.8330 | Alpha: 0.1420\n",
      "Pendulum-v1 Training Finished (SAC).\n"
     ]
    }
   ],
   "source": [
    "print(\"Starting SAC Training on Pendulum-v1...\")\n",
    "\n",
    "# --- SAC Training Loop ---\n",
    "total_steps_sac = 0\n",
    "for i_episode in range(1, NUM_EPISODES_SAC + 1):\n",
    "    state_np, info = env.reset()\n",
    "    state = torch.from_numpy(state_np).float().to(device)\n",
    "    episode_reward = 0\n",
    "    episode_critic_loss = 0\n",
    "    episode_actor_loss = 0\n",
    "    episode_alpha_loss = 0\n",
    "    num_updates = 0\n",
    "\n",
    "    for t in range(MAX_STEPS_PER_EPISODE_SAC):\n",
    "        # --- Action Selection --- \n",
    "        if total_steps_sac < START_STEPS:\n",
    "            # Initial exploration with random actions\n",
    "            action = env.action_space.sample() # Sample from the environment's action space\n",
    "            action_tensor = torch.from_numpy(action).float().to(device)\n",
    "        else:\n",
    "            # Sample action from the stochastic policy\n",
    "            actor_sac.eval() # Set to eval mode for consistent sampling\n",
    "            with torch.no_grad():\n",
    "                action_tensor, _ = actor_sac(state)\n",
    "            actor_sac.train() # Back to train mode\n",
    "            action = action_tensor.cpu().numpy() # Convert to numpy for env.step\n",
    "            # Action is already scaled by the network\n",
    "            # Clipping might still be needed if network output + noise slightly exceeds bounds\n",
    "            action = np.clip(action, action_low_sac, action_high_sac)\n",
    "\n",
    "        # --- Environment Interaction --- \n",
    "        next_state_np, reward, terminated, truncated, _ = env.step(action)\n",
    "        done = terminated or truncated\n",
    "        \n",
    "        # --- Store Experience --- \n",
    "        # Ensure action stored is a tensor\n",
    "        action_store_tensor = torch.from_numpy(action if isinstance(action, np.ndarray) else np.array([action])).float()\n",
    "        memory_sac.push(state, action_store_tensor, reward, next_state_np, done)\n",
    "\n",
    "        state_np = next_state_np\n",
    "        state = torch.from_numpy(state_np).float().to(device)\n",
    "        episode_reward += reward\n",
    "        total_steps_sac += 1\n",
    "\n",
    "        # --- Update Networks (if enough steps gathered and buffer full enough) --- \n",
    "        if total_steps_sac >= START_STEPS and total_steps_sac % UPDATE_EVERY_SAC == 0:\n",
    "            if len(memory_sac) > BATCH_SIZE_SAC:\n",
    "                c_loss, a_loss, alpha_loss, _ = update_sac(\n",
    "                    memory_sac, BATCH_SIZE_SAC, \n",
    "                    actor_sac, critic_sac, target_critic_sac,\n",
    "                    actor_optimizer_sac, critic_optimizer_sac,\n",
    "                    log_alpha_sac, alpha_optimizer_sac if AUTO_TUNE_ALPHA else None, \n",
    "                    TARGET_ENTROPY if AUTO_TUNE_ALPHA else 0.0,\n",
    "                    GAMMA_SAC, TAU_SAC\n",
    "                )\n",
    "                episode_critic_loss += c_loss\n",
    "                episode_actor_loss += a_loss\n",
    "                episode_alpha_loss += alpha_loss\n",
    "                num_updates += 1\n",
    "\n",
    "        if done:\n",
    "            break\n",
    "            \n",
    "    # --- End of Episode --- \n",
    "    sac_episode_rewards.append(episode_reward)\n",
    "    sac_episode_critic_losses.append(episode_critic_loss / num_updates if num_updates > 0 else 0)\n",
    "    sac_episode_actor_losses.append(episode_actor_loss / num_updates if num_updates > 0 else 0)\n",
    "    sac_episode_alpha_losses.append(episode_alpha_loss / num_updates if num_updates > 0 else 0)\n",
    "    sac_episode_alphas.append(torch.exp(log_alpha_sac.detach()).item())\n",
    "\n",
    "    # Print progress\n",
    "    if i_episode % 10 == 0:\n",
    "        avg_reward = np.mean(sac_episode_rewards[-10:])\n",
    "        avg_closs = np.mean(sac_episode_critic_losses[-10:])\n",
    "        avg_aloss = np.mean(sac_episode_actor_losses[-10:])\n",
    "        current_alpha = sac_episode_alphas[-1]\n",
    "        print(f\"Ep {i_episode}/{NUM_EPISODES_SAC} | Steps: {total_steps_sac} | Avg Reward: {avg_reward:.2f} | C_Loss: {avg_closs:.4f} | A_Loss: {avg_aloss:.4f} | Alpha: {current_alpha:.4f}\")\n",
    "\n",
    "print(\"Pendulum-v1 Training Finished (SAC).\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Learning Process\n",
    "\n",
    "Plot episode rewards, losses, and the learned alpha value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting results for SAC on Pendulum-v1\n",
    "plt.figure(figsize=(20, 8))\n",
    "\n",
    "# Episode Rewards\n",
    "plt.subplot(2, 3, 1)\n",
    "plt.plot(sac_episode_rewards)\n",
    "plt.title('SAC Pendulum: Episode Rewards')\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Total Reward')\n",
    "plt.grid(True)\n",
    "if len(sac_episode_rewards) >= 10:\n",
    "    rewards_ma_sac = np.convolve(sac_episode_rewards, np.ones(10)/10, mode='valid')\n",
    "    plt.plot(np.arange(len(rewards_ma_sac)) + 9, rewards_ma_sac, label='10-episode MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "# Critic Loss\n",
    "plt.subplot(2, 3, 2)\n",
    "plt.plot(sac_episode_critic_losses)\n",
    "plt.title('SAC Pendulum: Avg Critic Loss / Episode')\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Avg MSE Loss')\n",
    "plt.grid(True)\n",
    "if len(sac_episode_critic_losses) >= 10:\n",
    "    closs_ma_sac = np.convolve(sac_episode_critic_losses, np.ones(10)/10, mode='valid')\n",
    "    plt.plot(np.arange(len(closs_ma_sac)) + 9, closs_ma_sac, label='10-episode MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "# Actor Loss\n",
    "plt.subplot(2, 3, 3)\n",
    "plt.plot(sac_episode_actor_losses)\n",
    "plt.title('SAC Pendulum: Avg Actor Loss / Episode')\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Avg Loss (alpha*log_pi - Q)')\n",
    "plt.grid(True)\n",
    "if len(sac_episode_actor_losses) >= 10:\n",
    "    aloss_ma_sac = np.convolve(sac_episode_actor_losses, np.ones(10)/10, mode='valid')\n",
    "    plt.plot(np.arange(len(aloss_ma_sac)) + 9, aloss_ma_sac, label='10-episode MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "# Alpha Value\n",
    "plt.subplot(2, 3, 4)\n",
    "plt.plot(sac_episode_alphas)\n",
    "plt.title('SAC Pendulum: Alpha (Entropy Temp) / Episode')\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Alpha')\n",
    "plt.grid(True)\n",
    "\n",
    "# Alpha Loss (if auto-tuning)\n",
    "if AUTO_TUNE_ALPHA:\n",
    "    plt.subplot(2, 3, 5)\n",
    "    plt.plot(sac_episode_alpha_losses)\n",
    "    plt.title('SAC Pendulum: Avg Alpha Loss / Episode')\n",
    "    plt.xlabel('Episode')\n",
    "    plt.ylabel('Avg Loss')\n",
    "    plt.grid(True)\n",
    "    if len(sac_episode_alpha_losses) >= 10:\n",
    "        alphloss_ma_sac = np.convolve(sac_episode_alpha_losses, np.ones(10)/10, mode='valid')\n",
    "        plt.plot(np.arange(len(alphloss_ma_sac)) + 9, alphloss_ma_sac, label='10-episode MA', color='orange')\n",
    "        plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis of SAC Learning Curves (Pendulum):**\n",
    "\n",
    "1.  **Episode Rewards:**\n",
    "    The agent shows clear and relatively stable learning, with the moving average reward increasing significantly from around -1500 to approximately -200 within the first 40-50 episodes. The rewards plateau at a high level, indicating successful policy optimization for the pendulum swing-up task. The variance appears somewhat lower than DDPG, likely due to SAC's entropy regularization promoting smoother policy changes.\n",
    "\n",
    "2.  **Avg Critic Loss / Episode:**\n",
    "    Similar to DDPG, the critic's MSE loss increases substantially during training. This reflects the critic adapting to the improving policy and higher target Q-values associated with better performance (less negative rewards). While rising loss can sometimes indicate instability, here it likely corresponds to the increasing scale of Q-values in this task, and the stable rewards suggest the critic learning is functional.\n",
    "\n",
    "3.  **Avg Actor Loss / Episode:**\n",
    "    The actor loss (representing `alpha*log_pi - Q`) increases initially and then gradually decreases after peaking around episode 40. This complex objective aims to maximize both expected return (high Q) and policy entropy (high log\\_pi). The peak likely corresponds to the point where entropy regularization (`alpha*log_pi`) dominates, after which the focus shifts towards maximizing Q-values, causing the overall loss (which is minimized, so the objective is maximized) to decrease.\n",
    "\n",
    "4.  **Alpha (Entropy Temp) / Episode:**\n",
    "    The automatic entropy tuning parameter, alpha, exhibits interesting behavior. It decreases initially, suggesting the agent is quickly becoming more confident (reducing entropy), then increases significantly until episode ~55, indicating a need for more exploration perhaps to escape a local optimum or adapt to higher value states, before finally decreasing again as the policy converges and becomes more deterministic. This dynamic adjustment is key to SAC's exploration-exploitation balance.\n",
    "\n",
    "5.  **Avg Alpha Loss / Episode:**\n",
    "    The loss associated with tuning alpha shows an initial negative spike, then rises and stabilizes close to zero. This loss aims to drive alpha such that the policy entropy matches a target value. Its stabilization near zero indicates the automatic tuning mechanism has converged, successfully balancing the policy's entropy according to the target.\n",
    "\n",
    "**Overall Conclusion:**\n",
    "SAC demonstrates effective and relatively stable learning on the continuous Pendulum task, achieving high rewards comparable to DDPG but potentially with less reward variance. The automatic entropy tuning (alpha) actively adjusts the exploration level throughout training, contributing to robust learning. The critic and actor losses show trends consistent with SAC's maximum entropy objective and the dynamics of the Pendulum environment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing the Learned Policy (Testing)\n",
    "\n",
    "Visualize the performance of the trained SAC agent by running it deterministically (using the mean action from the policy distribution) in the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Testing SAC Agent (3 episodes, Deterministic) ---\n",
      "Test Episode 1: Reward = -0.47, Length = 200\n",
      "Test Episode 2: Reward = -123.35, Length = 200\n",
      "Test Episode 3: Reward = -0.76, Length = 200\n",
      "--- Testing Complete. Average Reward: -41.53 ---\n"
     ]
    }
   ],
   "source": [
    "def test_sac_agent(actor_net: ActorNetworkSAC, \n",
    "                   env_instance: gym.Env, \n",
    "                   num_episodes: int = 5, \n",
    "                   render: bool = False, \n",
    "                   seed_offset: int = 2000) -> None:\n",
    "    \"\"\"\n",
    "    Tests the trained SAC agent using the mean action (deterministically).\n",
    "    \"\"\"\n",
    "    if env_instance is None:\n",
    "        print(\"Environment not available for testing.\")\n",
    "        return\n",
    "        \n",
    "    actor_net.eval() # Set actor to evaluation mode\n",
    "    \n",
    "    print(f\"\\n--- Testing SAC Agent ({num_episodes} episodes, Deterministic) ---\")\n",
    "    all_rewards = []\n",
    "    for i in range(num_episodes):\n",
    "        state_np, info = env_instance.reset(seed=seed + seed_offset + i)\n",
    "        state = torch.from_numpy(state_np).float().to(device)\n",
    "        episode_reward = 0\n",
    "        done = False\n",
    "        t = 0\n",
    "        while not done:\n",
    "            if render:\n",
    "                try:\n",
    "                    env_instance.render()\n",
    "                    time.sleep(0.01)\n",
    "                except Exception as e:\n",
    "                    print(f\"Rendering failed: {e}. Disabling render.\")\n",
    "                    render = False\n",
    "            \n",
    "            with torch.no_grad():\n",
    "                # --- Get Deterministic Action (Mean) --- \n",
    "                # Forward pass to get mean, ignore sampled action and log_prob\n",
    "                x = F.relu(actor_net.layer1(state))\n",
    "                x = F.relu(actor_net.layer2(x))\n",
    "                mean = actor_net.mean_layer(x)\n",
    "                action_deterministic = torch.tanh(mean) * actor_net.action_high_bound\n",
    "                # -----------------------------------------\n",
    "                action = action_deterministic.cpu().numpy()\n",
    "            \n",
    "            # Clipping just in case\n",
    "            action_clipped = np.clip(action, env_instance.action_space.low, env_instance.action_space.high)\n",
    "            \n",
    "            next_state_np, reward, terminated, truncated, _ = env_instance.step(action_clipped)\n",
    "            done = terminated or truncated\n",
    "            state = torch.from_numpy(next_state_np).float().to(device)\n",
    "            episode_reward += reward\n",
    "            t += 1\n",
    "        \n",
    "        print(f\"Test Episode {i+1}: Reward = {episode_reward:.2f}, Length = {t}\")\n",
    "        all_rewards.append(episode_reward)\n",
    "        if render:\n",
    "             env_instance.close()\n",
    "\n",
    "    print(f\"--- Testing Complete. Average Reward: {np.mean(all_rewards):.2f} ---\")\n",
    "\n",
    "# Run test episodes\n",
    "test_sac_agent(actor_sac, env, num_episodes=3, render=False) # Set render=True if desired"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Common Challenges and Solutions in SAC\n",
    "\n",
    "**Challenge: Hyperparameter Sensitivity (especially $\\alpha$)**\n",
    "*   **Problem:** Performance is sensitive to learning rates, $\\tau$, batch size, and particularly the entropy temperature $\\alpha$. A fixed $\\alpha$ might be too high (excessive exploration, slow convergence) or too low (insufficient exploration, suboptimal policy).\n",
    "*   **Solutions**:\n",
    "    *   **Automated Alpha Tuning:** Implementing the automatic tuning of $\\alpha$ based on a target entropy (as done in this notebook) is highly recommended and generally improves stability and performance across different tasks.\n",
    "    *   **Careful Manual Tuning:** If not using auto-tuning, $\\alpha$ needs careful manual tuning, often requiring experimentation.\n",
    "    * **Standard Defaults:** Start with common values (e.g., LR=3e-4, $\\tau$=5e-3, batch=256).\n",
    "\n",
    "**Challenge: Choice of Target Entropy $\\bar{H}$**\n",
    "*   **Problem:** When auto-tuning $\\alpha$, the choice of target entropy $\\bar{H}$ can influence the resulting policy's exploration level. The heuristic $\\bar{H} = -\\text{dim}(\\mathcal{A})$ is common but not always optimal.\n",
    "*   **Solutions**:\n",
    "    *   **Use Heuristic:** Start with $\\bar{H} = -\\text{dim}(\\mathcal{A})$.\n",
    "    *   **Experiment:** Try slightly different values for $\\bar{H}$ if performance is unsatisfactory.\n",
    "\n",
    "**Challenge: Implementation Details (Squashing Correction)**\n",
    "*   **Problem:** Forgetting to apply the correction term to the log-probability due to the `tanh` squashing function is a common implementation error that significantly impacts performance.\n",
    "  **Solution:** Ensure the log-probability calculation correctly subtracts $\\log(1 - \\tanh(z)^2)$, summed over the action dimensions.\n",
    "\n",
    "**Challenge: Sample Efficiency on Very Complex Tasks**\n",
    "*   **Problem:** While highly sample efficient due to off-policy learning, extremely complex environments might still require vast amounts of data.\n",
    "   **Solutions**:\n",
    "    *   **Distributed Training:** Use multiple actors collecting data in parallel (e.g., frameworks like RLLib).\n",
    "    *   **Model-Based RL:** Incorporate a learned model of the environment dynamics to generate additional simulated data.\n",
    "    * **Offline RL:** If sufficient pre-collected data is available, offline RL variants can be used.\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "Soft Actor-Critic (SAC) is a powerful off-policy actor-critic algorithm that excels in continuous control tasks by incorporating the maximum entropy framework. By optimizing for both reward and policy entropy, SAC encourages robust exploration and often achieves state-of-the-art performance with high sample efficiency and stability.\n",
    "\n",
    "Key features like the stochastic policy with reparameterization, twin Q-critics with soft target updates, and often automated tuning of the entropy temperature ($\\alpha$) contribute to its success. While still requiring careful hyperparameter tuning, SAC represents a significant advancement over DDPG and TD3, offering a compelling blend of performance, stability, and exploration capabilities, making it a popular choice for challenging continuous control problems in modern reinforcement learning."
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